Slurrification and disposal of waste by pressure pumping into a subsurface formation

ABSTRACT

Presented is a computerized method, apparatus, and systems for management of a slurry injection well and the associated surface facility. A network is provided for connectivity of facility equipment, sensor equipment, and control software operated on a computer system. The method utilizes real time and historical data of injection and slurry parameters in conjunction with computer simulations performed on a computer-modelled reservoir to predict well behavior during an injection event. The system determines optimized injection operation schedules and can recommend and implement changes to an injection operation while it is in process, including through automated equipment control.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is an International Patent Application (under auspices of the PCT)claiming priority to U.S. Provisional Application Ser. No. 62/019,083,filed Jun. 30, 2014.

TECHNICAL FIELD

The disclosure relates generally to the oil and gas industry and otherindustries that produce fluid and solid waste, namely to management of aslurry injection well and the associated surface facility. Moreparticularly, the disclosure relates to utilizing a combination ofdata-mining, engineering, analysis, and software for subsurfacesimulation to optimize and execute slurrification and disposal bypressure pumping into a subsurface formation.

BACKGROUND

Drilling into (and hydraulic fracturing of) subterranean zones to enableand or enhance recovery of hydrocarbons is now common practice in theoil and gas industry. However, drilling and other oil and gas productionprocesses often result in massive amounts of waste which must bedisposed. The waste can contain drilling solids and fluids, stimulationtreatment fluids, sand, fines, and other solids from the wellbore,proppant and chemicals from hydraulic fracturing, produced water, brine,hydrocarbons and other fluid and solid components accumulated during oiland gas production. Waste streams from numerous other industries(including but not to waste from chemical manufacturing, waste fromlandfills, waste from residential sources, waste from refiningpractices, radioactive waste generated during recycling and processing,and biological and or other hazardous wastes) also lead to fluid andsolid waste types that can be managed using the disclosed methods. Onemethod of disposing of these waste streams is injection into asubterranean zone using a disposal well. In some methods of disposal,the disposal well is completed in one or more subterranean zones thatare fractured or re-fractured during injection.

As with oil and gas production wells, it is useful to know or estimatevarious properties of the zones that may be used for injection, insofaras these properties may directly or indirectly affect the impact ofinjection on the surrounding reservoir volume, containment of theinjected fluids, and fracture behavior during and after injection. Ithas also become important to estimate which of the properties can beused to identify targeted zones for injection, and how these propertieschange over time. The operating procedures used to carry out injectionoperations (which lead to fracture behaviors) are tightly coupled withthe subsurface geology and its dynamic response to injection. A robustmethodology that accounts for this coupling, and which includesreal-time feedback to enables and provide directional changes to theoperating procedures, helps ensure safe and optimal disposal.

BRIEF DESCRIPTION OF THE DRAWING

For a more complete understanding of the features and advantages of thepresent disclosure, reference is now made to the detailed description ofthe disclosure along with the accompanying figures in whichcorresponding numerals in the different figures refer to correspondingparts and in which:

FIG. 1 is a schematic of an exemplary waste disposal facility, generallydesignated 10, according to an aspect of the disclosure.

FIG. 2 is an exemplary flow chart for an Economic Feasibility FEED studyaccording to aspects of the disclosure.

FIG. 3 is a flow chart of an exemplary FEED study, generally designated150, to determine Geologic Feasibility for a proposed SIF according toan aspect of the disclosure.

FIG. 4 is an exemplary chart indicating lithology of plotted well logdata points according to an aspect of the disclosure.

FIG. 5 is a flow chart of an exemplary FEED study, generally designated200, to determine Geologic Feasibility for a proposed SIF according toan aspect of the disclosure.

FIG. 6 is a flow chart of an exemplary computerized Well ManagementSystem (WMS) method, generally designated 300, to actively andautomatically manage an operating waste injection well and wastedisposal facility.

DETAILED DESCRIPTION

The present disclosures are described by reference to drawings showingone or more examples of how the disclosures can be made and used. Inthese drawings, reference characters are used throughout the severalviews to indicate like or corresponding parts. In the description whichfollows, like or corresponding parts are marked throughout thespecification and drawings with the same reference numerals,respectively. Figures are not intended to be to-scale.

System and Facility Overview

The disclosure identifies a preferred sequence, combination, andcoupling of computerized methods for performing Front-End EngineeringDesign (FEED) studies and active operational management of a wasteslurry injection well, before, during, and after injection, and of theassociated surface facility.

The disclosed FEED study and related methods and apparatus utilize acombination of data-mining, engineering, analysis, and software forcomputerized subsurface simulation (computer modelling) to optimize andexecute slurrification and disposal of waste by pressure pumping into,and often fracturing of, a zone of a subsurface formation.

The methods disclosed provide a system-wide coordination of activitiesand metrics for: deciding among a plurality of sites which are optimalfor locating a disposal facility based on economic market conditions andlocal regulations; deciding which geologic formations may be used forinjection; forecasting the subsurface disposal capacity under variousoperating conditions; determining surface storage requirements toaccommodate the most cost-effective types and volumes of various wastestreams; and definition of additional useful metrics, one of which iscalled an “Operating Window,” which provides operators with anunderstanding of how best to interpret subsurface injection behavior inview of the site's Maximum Allowed Surface Injection Pressure (MASIP).The MASIP may be limited by facility, equipment, regulation, results ofthe methods disclosed herein, or any combination of these constraints.

The disclosed methods include a computerized Well Management System toidentify anomalous and or critical injection behaviors that can affectthe optimized results, well management, and safety. The Well ManagementSystem utilizes real-time and archived data measured at the well,facility surface equipment, or surrounding area, including one or moreof the following: the time-dependent surface tubing, annular andbottom-hole well pressure (measured at one or more positions along thewell, and in particular, above where injection into the rock occurs),injection flow rate, injected fluid rheology (including, but not limitedto, temperature and solids concentration-dependent density andviscosity), surface tank volumes, earthquake and or seismic activity,and other sensors placed temporarily or permanently inside or outsidethe well or at neighboring offset wells (to measure, for example,temperature and acoustic emissions inside the reservoir). The real-timeand or archived data can be used by the Well Management System to updatethe method's optimized injection parameters so that waste disposal ismaximized and risk associated with injection is minimized.

The description herein is directed, primarily, to injection of wasteslurry into one or more subsurface zones. Notwithstanding anything elseherein, the methods and apparatus with regard to the control system,injection simulations, real time process monitoring, on-going softwareoperation and updated parameters, and automated equipment operation canbe applied to, with minor modification in some cases, injectionoperations more broadly including water flooding to maintain reservoirpressure in producing wells and injection to create hydraulic fracturesfor stimulation of production wells.

Slurry Injection Facility

FIG. 1 is a schematic of an exemplary waste disposal facility, generallydesignated 10, according to an aspect of the disclosure. The disclosedmethods enable safe and cost-effective operation of waste disposal andprocessing facilities which utilize subsurface, injection. An optimallydesigned facility 10 enables operators to receive high volumes of avariety of waste streams (solid, fluid, and mixtures thereof). In casethe waste streams are from oil and gas Exploration and Production (E&P),the wastes typically consist of drill cuttings, drilling fluids,drilling muds, completion fluids, production fluids, waste-water, tankbottoms, emulsions, wash-down fluids, truck wash fluids, and/or othersliquids, solids and mixtures. Wastes are typically received at a land oroff-shore site that serves as the waste facility, prepared into a stable“slurrified,” non-sedimenting mixture (slurry), and injected in one or aseries of batches into pre-determined sub-surface disposal zones. Thewaste facility 10 is shown as a land-based facility, without limitation.

The facility utilizes a series of stages or staging areas for receivingand handling waste. Tanks or suitably designed in-ground or above-ground“pits” 11 ensure that various wastes make minimal or no contact with theexternal environment during the process.

Stages

Stages, staging areas, and sub-systems can include one or more of: wasteseparation and classification equipment 12, slurry preparation andmixing equipment 14, temporary storage equipment 16 (e.g., holding oragitation tanks until injection is ready to begin), pre-injectionslurry-concentrate preparation and injection slurry preparationequipment 18 (e.g., dilution equipment), waste and slurry transportationequipment 20 between staging areas (e.g., pipes, pumps, tanks, trucks).These processes and equipment are known in the art.

Exemplary facility equipment 13 can include scrubbers, separators,mixers, blenders, agitators, slug catchers, filter separators,coalescers, knockout systems, piping, hoses, valving (float, gate,etc.), supply and storage tanks, bins and towers, pumps (transfer, frac,dilution, recycle, etc.), generators, actuators, dehydrators, thermaltreatment systems, vacuum systems, compressors, stim equipment, andinstrumentation and controls. Conceptually, the equipment can be dividedinto groups by function; for example, incoming waste stream managementand treatment equipment, slurrification equipment, injection equipment(with or without slurry-concentrate dilution for injection), etc.

Slurry Ratios

“Slurry-concentrate” or pre-injection slurry can be stored atpre-injection concentration ratios (or ranges), such as at storage stage16, above target injection ratios (or target injection ratio range).Fluid preparation at stage 14 ensures the slurry-concentrate is stable.The prepared slurry-concentrate is checked for quality control to ensureslurry concentrate properties are within a selected range of parametervalues. Sensed and derived parameters can include slurry concentrateviscosity, density, pressure, volume, flow rates, mix rates,temperatures, etc.

The pre-injection ratio or range is determined from geomechanicalcomputer modelling. In particular, the slurry viscosity, density, andmicroscopic component sizes are tested to confirm both homogenizationand control of particle size.

Where slurry concentration processes are used, the slurry concentrate isconverted, prior to or at the time of injection, to injection slurryhaving the injection concentration ratios or ranges specified. Whenoperators are ready to begin an injection operation, the pre-injectionslurry concentrate at 16 is transported to a slurry preparationsub-system 18 where water or other diluents are added and mixed tocontrol slurry rheology prior to high-pressure pumping into a disposalwell. The slurry concentrate is diluted to a target injection ratio orrange, tested for quality control using real-time networked, sensorequipment for parameter measurement, compared against the chosenparameters, and possibly modified so that the parameters have thedesired values or are in the desired ranges.

Injection Cycle

The injection slurry is pumped in batches via high-pressure pumps 34 andassociated surface equipment into the injection wellbore 38 and thenceto pre-selected subsurface disposal zone(s) 42. High-volume batchinjections ensure that the reservoir formation accommodates largevolumes of waste slurry from the surface.

The injection slurry is injected into the disposal well 36, via awellbore 38, which can be cased 40 along all or part of its length,extending through various earth strata, including at least one disposalzone 42. The disposal zone 42 is bounded by upper and lower boundaryzones 44, 46. Disposal operations and hydraulic fractures 51 should notbreach the boundary zones 44, 46 and disposal waste slurry should remainwithin the targeted disposal zone both during and after injectionoperations. Short and long-term storage or disposal should both accountfor limiting slurry breach of boundary zones. Appropriate pumping 34,valving, and surface equipment 13 are utilized in accordance withindustry practice.

Sensor Equipment, Network

Testing, measurement, and quality control processes include use ofvarious sensor equipment at the processing, pre-injection, injection,and post-injection stages. According to an embodiment, a plurality ofsensor equipment 22 is used to gather, store, and transmit measured data24.

As used herein, sensor equipment includes digital and analog sensorequipment and smart sensor equipment such as known in the art. Smartsensors measure parameters (or their surrogates), and also performcalculations and derivations, logic routines, automated sensing,automated and “upon request” data transmission (e.g., push or pull),handle communication via the network (e.g., creating content,compressing, routing, verifying, etc.), and/or provide automated alerts,notifications, and warnings to the operator, and/or can provideautomated or on-demand control of facility materials-handling and wellequipment. Smart sensors typically have associated computer and memorydevices, software or logic programs, communications systems, etc., forcontrolling sensor operation, reading and storing measured data,performing quality control and verifications, etc. The smart sensorscommunicate measured data via wire or wireless transmission toconnected, networked computer control systems, local or remote from thefacility.

Measured data 24 can be provided to the computerized control system 28in real-time, intermittently, on-demand, or contingently upon occurrenceof selected criteria. The data can be recorded, stored, manipulated,archived, etc., at any point in the network. Sensor equipment includessensors (e.g., temperature), meters (e.g., flow rate),equipment-monitoring sensors (e.g., oil pressure), testing sensors(e.g., pH sensors), etc., as well as the associated computerizeddevices, hardware, and software for measuring, storing, andcommunicating measured data.

As used herein, the measured data 24 can include direct parametermeasurements (e.g., injection flow rate, slurry viscosity, slurrydensity, injection time, wellhead and downhole pressures, wellhead anddownhole temperatures, time elapsed during injection, acoustic andelastic wave events generated by subsurface dynamics and monitored bygeophones placed at the surface or in neighboring offset wells etc.) anddata calculated or derived therefrom (e.g., pressure loss between thewellhead and downhole injection zones, predictions of concentrationchanges along the wellbore, simulations of the resulting injection andreservoir performance and fracturing behaviors, predictions of changesto the reservoir properties caused by the process of injection, etc.).

The computerized system or network 26 can utilize existinginfrastructure such as the internet, web, telephony systems, etc. Thenetwork includes various elements, as are known in the art, such asservers, routers, gateways, databases, and computer devices, which areconnected for data communication.

Data can be collected, stored, analyzed, and used in software programsexecuted from the computerized control system 28, at the sensors 22, 52,or a combination thereof, locally or remotely, using real-time data orarchived data from data storage or non-transitory memory devices 30operably connected to one or more computers 32 on which a softwareprogram is executed.

The computerized control system 28 includes non-transitory memorydevices 30, computer devices 32, input and output devices (e.g.,screens, keyboards), and is connected via network 26 to facilityequipment, sensor equipment, etc. The computerized control system can belocalized or dispersed. One or more software programs are executable bythe system 28 for communicating, storing and accessing measured data,running injection operation simulations, modelling the reservoirinjection 42, boundary 44,46, and other zones, calculating injection andreservoir parameters and behaviors, performing Front End EngineeringDesign studies for geologic, operational and economic feasibility, etc.,where appropriate. Further, the control system 28 is operable tocommunicate commands to and operate facility equipment, such asinjection pumps, slurry dilution equipment, etc.

Stage Sensors

Representative sensor equipment 22 is shown at each stage of thefacility process. Sensor equipment can provide measurements and measureddata such as density, flow rate, volumes, pressures, mixing rates, rateof parameter changes, viscosity, temperatures, particulate and solidssize, and other parameters, at any of the stages. Although a singlepiece of sensor equipment 22 is shown at each stage, it is understoodthat each is representative and that typically multiple sensorequipment, measuring numerous parameters, and associated with numerousequipment will actually be in use. The sensor equipment 22 can supplydiscrete, continuous, automated, and/or real-time measurements of wasteand other fluid and mixture volumes, flow rates, mix rates, density,viscosity, and other parameters as known in the art, as well as dataregarding the operational parameters of the facility machinery.

Similarly, data 24 is indicated representatively at each stage althoughit is expected that measured data will be communicated from multiplepieces of facility equipment, sensor equipment, etc., at each stage andacross the facility.

Injection Operation Sensors

As with the waste management and slurry preparation stages, sensorequipment 52 can be used before, during, and after a batch injectionoperation. The sensor equipment 52 can supply measurements of slurryvolumes, other fluid volumes, as well as slurry concentrate, injectionslurry, and other fluid parameters (e.g., viscosity, density, etc.), andsystem pressures (e.g., injection and well pressures), injection andflow rates, temperatures, and other parameters. The sensor equipment 52indicated in FIG. 1 is representative. In actuality, multiple sensorequipment, associated with multiple facility equipment, testingstations, etc., are employed during an injection operation.

In an embodiment, real-time data is communicated between the sensorequipment and control system. The real-time data is input to softwareoperable on the computer control system 28 and, combined with historicaland previously input data, the software runs complicated analyses of thecomputer-modelled injection operation, injection zone, and boundaryzones, to provide near real-time analysis of the batch injection on theformation.

Real-Time Computer Modelling and Analysis

Such data is used to facilitate safe and coordinated operations and theexecution of computerized analyses which provide a physical descriptionof the response of the injection zone and surrounding reservoir to theinjection. These measurements and the analyses they facilitate are usedfor optimizing well performance, ensuring control of subsurfacemigration of fluids, and permitting verification of containment andenvironmentally safe operations. The software can utilize real-timedata, supplied from sensor equipment at the well or surface equipment,and historical data. Similarly, the software can be executed before,during, or after an injection operation, and can provide resultingoutput (e.g., maximum pump pressure, optimal injection rates anddurations, optimal batch designs, modifications to designs based onsubsequent input, etc.) before, during, or after injection.

The software program identifies injection parameters (e.g., pumppressure, flow rate, slurry density, slurry viscosity, etc.) which, ifchanged, are most likely to eliminate or mitigate potential damage. Theidentified parameters are varied and iterative simulations run,according to a convergence formula, to provide an optimal injectionoperational plan, injection batch design, or other sought output. Whererun prior to injection, the operational plan is placed into effect. Thesimulations utilize measured and historical data as inputs forperforming the simulated injection operations.

Alternately, real-time measured data from the injection operation iscommunicated to and used by the software program. The real-time computermodelling performed by the software can provide results indicating howthe injection operations should proceed (according to the batch design)or that operational modifications can or must be made. For example, areal-time injection simulation may indicate an unforeseen or developingpotential risk (e.g., loss of containment by fracture growth into aboundary zone or slurry migration) should injection operations continueunabated. Other predetermined limitations may be implicated by real-timesimulations, such as regulations, operational hazards, other types offormation damage which negatively impacts current or future injections,etc. Alternately, the computer simulations may result in potentialchanges or modifications to the injection operation or batch design tobe made on-the-fly to optimize waste disposal, operating time, fracturecreation, etc. (e.g., increased injection rate, higher injectionpressures, etc.).

The software program runs numerous, iterative, injection operationsimulations, updating the simulations with real-time data. The computedresults indicate, in an example, that maximum slurry disposal requiresan increase in injection rate (or other parameter) to obtain aconcomitant rise in downhole pressure. The pressure rise, in turn,impacts the injection-induced hydraulic fracture behaviors in one ormore subsurface zones. The software program finds and indicates anincrease in injection rate necessary to reach the newly-maximizedfractures and disposal volumes, without loss of containment or violationof other operational limitations (e.g., regulatory limitations).

Other exemplary results might indicate that slurry density should bedecreased for a period of time to change a forecasted hydraulic fracturewith non-optimal dimensions (length, width, height). The program outputsdata regarding proposed on-the-fly slurry density reduction which, ifperformed, will result in one or more desired fracture dimensions. Thesimulations may indicate that injection parameters should be changedfrom one sequence of slurry properties or injection rates to anothersequence so that, for example, volumes of highly concentrated slurry canbe preferentially swept deep into an existing fracture.

Further, the software may identify (previously unknown) reservoirfeatures, away from the wellbore, that are hypothesized to exist inorder to satisfactorily correlate current observed injection behaviorswith simulation-predicted behaviors. For example, the computersimulation program can conclude, based on measured (and input) data,that unexpected scenarios exist, such as, but not limited to, highlypermeable conduits for fluid flow, natural fractures, regions of highgas saturation (whose physical motion impacts leak-off of fluid from theinjection well), etc. The simulations may indicate that the negativeinjection behaviors associated with the real-time measurement arerelated to such reservoir features.

In case natural fractures are predicted to be present, for example, thesimulations can indicate that a significant drop in wellhead fluidpressure, caused by rapid leak-off into a natural fracture system, canbe mitigated by increasing slurry concentration, on-the-fly, toeffectively plug the natural fractures where they intersectinjection-induced fractures.

Utilizing simulations of the wellbore, the simulator predicts howidentifiable volumes of waste may be delivered to multiple,simultaneously active, subsurface zones. The program's analysis oftransient behavior of well-head pressure and other measured data, canalso indicate which subsurface zone is undergoing fracture, how manyfractures are simultaneously propagating, whether the fractures interactwithin the reservoir, whether and how waste slurry may commingle withinthe reservoir, etc. As another example, if the program, based onreal-time measured data, concludes that one or more desired fracture hasceased to propagate, the program can indicate a change in injection rateand slurry properties (e.g., increase in injection rate, decrease inslurry viscosity) for a calculated period (seconds, minutes, other) tore-initiate fracturing.

Real-time analysis is only possible due to the speed and power of thecomputer and software. Potentially thousands of algorithms,calculations, comparisons, iterative functions, and logical instructionsmust be run in extremely short time periods on the order of minutes oreven seconds to provide results quickly enough to enable on-the-flychanges in injection operations. In an embodiment, the software analyzesreal-time data at a rate of more than one megabyte per second (MB/s) andruns simultaneously on multiple high-speed processors, which eachprocess data at more than 100 billion Floating-point Operations PerSecond (GFLOPS). These processor speeds and data bandwidth managementcapabilities are critical to the method's success and are orders ofmagnitude larger than what can be performed by one or more humanexperts. The enabling technology allows the simulator to predictwellbore, reservoir, and hydraulic fracturing behavior for a range ofpotential input variables, in real-time, near real-time, or otherwisequickly enough, so that injection operational parameters can beidentified, selected, and updated to optimize performance.

Automated Equipment Operation

The methods can also include fully or partially automated equipmentcontrol in some embodiments. The network 26 provides connectivitybetween facility equipment, sensor equipment 22, 52, and the controlsystem 28. Injection Performance Simulator (IPS) to simulate matrixinjection and hydraulic fracturing

Equipment controllers 15 are commercially available and known in the artand often comprise a computer device, having memory and processing, andvarious subsystems (communication, monitoring, metering, datamanagement, safety overrides, etc.). Controllers control the equipment,performing tasks (e.g., on/off, power up/down, open/close, etc.) on theequipment. Controllers typically include user interfaces (e.g., screens)allowing monitoring, simple programming, and semi-automated equipmentcontrol. The controller provides connectivity to the network andcomputer control system, Common network protocols (e.g., LonWorks,BACnet, Modbus, etc.) can be used. Controllers can have multiplecommunication ports, wired and/or wireless, for connectivity to thenetwork, and can communicate via Ethernet, OPC, BACnet IP, Modbus TCP,SNMP, or other protocol.

Relatedly, the network can include gateways associated with the sensorequipment (meters, sub-meters, smart meters, and Data Recorder meters,etc.), equipment controllers (e.g., generator or pump gateways), etc. Agateway can be a physical unit connected to equipment or a softwareapplication running at some level of the network. Controllers caninclude or serve as gateways, providing connectivity integrally withother services of the device.

The facility network can have multiple sub-networks for variousfunctions and controls. The network provides connectivity betweenfacility equipment, sensor equipment, operator computers, the computercontrol system, etc. The network can employ multiple users, buildings,systems, etc. Network communication can be between: gateways andequipment; pieces of equipment; gateways; equipment and the internet;etc.

Exemplary equipment, representatively seen as pump 34, which can becontrolled from the computerized control system at the direction of theIPS program or associated control software include all electricalequipment at the site. For example, surface equipment can includescrubbers, separators, mixers, blenders, agitators, slug catchers,filter separators, coalescers, knockout systems, piping, hoses, valving(float, gate, etc.), supply and storage tanks, bins and towers, pumps(transfer, frac, dilution, recycle, etc.), generators, actuators,dehydrators, thermal treatment systems, vacuum systems, compressors,stim equipment, and instrumentation and controls. Conceptually, theequipment can be divided into groups by function. For example, incomingwaste stream management and treatment equipment, slurrificationequipment, well or injection equipment (with or withoutslurry-concentrate dilution for injection). Some equipment, such as agenerator or a pump, can include sensors (e.g., temperature, oilpressure, etc.) for monitoring the equipment, and a gateway orcontroller for managing and communicating resulting data.

Facility automation is controlled by the control system 28. Based onreal time or historical measured data, pulled from current monitoring orarchives, and the current operation (e.g., injection, slurrification),the control system communicates operating instructions or tasks to theindicated equipment. For example, when a slurrification procedure isunderway according to a design or schedule input to or created by thesystem, monitored sensor equipment may provide measured data regardingcurrent slurry parameters (e.g., viscosity). The control system andsoftware compares monitored and planned parameters, determines processesto correct current parameters to match the planned parameters. Thesystem calculates necessary operating parameters for the correction(e.g., amounts, rates, materials, etc.) and determines correspondingtasks for facility equipment to undertake. In this example, the systemcorrects an errant slurry viscosity by communicating task instructions(e.g., by signals, data, computer code, etc.), via the network, tocorresponding facility equipment, and operating the equipment toincrease a fluid flow rate, activate a mixer, etc., to determinedoperating parameters (e.g., rates, speeds, percentage power, etc.).Further monitoring by the system can confirm achievement of plannedviscosity or indicate a need for further tasks.

In an embodiment, the control system provides recommended tasks to theoperator (e.g., increase water flow from a supply pipe to a suggestedrate), communicated via the network to an operator station. In oneembodiment, the operator then makes those changes manually or via localcontroller. In another embodiment, the operator either allows ordisallows the recommended tasks. If allowed, the control systemautomatically operates the equipment accordingly. Operation of equipmentrequires connectivity and communication through the network andappropriate controllers and gateways. The controllers and gatewaysreceive and interpret incoming control messages 19 and perform theaction on the equipment. Thus, the computerized control service receivesand analyzes incoming data, and if indicated takes one or more actions,communicates with facility equipment gateways and/or controllers, andactually alters and controls the operation of facility equipment,thereby changing the operating parameters of an on-going operation.

Further automation examples are provided below regarding use of realtime data to run injection simulations, calculate behavior forecasts,identify potential problems or opportunities, determine appropriatechanges to planned schedules to avoid issues or take advantage ofopportunities, and then, in some embodiments, automatically operate theimplicated equipment at the site to make the necessary changes.Monitoring for verification and performing iterative correctivecalculations and equipment actuation is also contemplated.

The facility automation services can be performed on platforms usingcloud computing. Commercially available services providing a platform,infrastructure, and software are available such as cloud solutionsoffered by the major web service companies. Unless otherwise claimed,the particular location and type of infrastructure, hardware, software,hierarchy, etc., is not critical. Persons of skill in the art willrecognize additional distributed and local systems capable of performingthe services and functions described herein. Services can be provided asSoftware as a Service, as explained elsewhere herein.

The computer control system and associated software can alternately beconsidered a service provider capable of implementing services for andat the facility. The software continues to gather, store, and analyzedata, monitoring facility equipment, performing injection operationsimulations, determining batch injection schedules, and, in someembodiments, performing real time analysis based on gathered real timedata to determine expected reservoir behavior and, depending on theresults, recommending corrective or alternate actions, and/orautomatically operating facility equipment to implement therecommendations.

SUMMARY

In summary, a disclosed method provides an optimal FEED study andoperating method to coordinate activities and provide metrics for use infacility and injection well siting, disposal zone selection, surfacefacility design, and on-going injection operations to optimize facilityoperation, disposal capacity, and operational safety.

An exemplary FEED study for a proposed Slurry Injection Facility (SIF)evaluates a project's feasibility in one or more of the followingcategories: 1) Economic Feasibility, 2) Geologic Feasibility, and 3)Operational Feasibility. The Operational Feasibility often depends onlocal regulations for operating an injection well, which may restrictMaximum Allowable Surface Injection Pressure (MASIP), total dailyvolumes, placement of the injection interval (within one or more zones),and facility design and location.

Economic Feasibility

An exemplary FEED study according to embodiments of the disclosureincludes a study of Economic Feasibility for a proposed Slurry InjectionFacility (SIF).

FIG. 2 is an exemplary flow chart for an Economic Feasibility FEED studyaccording to aspects of the disclosure. Economic Feasibility analysisbegins at block 100 and can include identification and analysis ofgeographic information re: a targeted geographical area at 102,including the locations of one or more waste sources (e.g., sites whereE&P drilling is occurring or may be expected to occur, E&P wells thatproduce water or brine that needs to be disposed, etc.). Identificationand analysis of the routes and route properties at 104 (e.g., distance,drive-time, easements, rights of way, pipeline tie-ins, land ownership,etc.) between each waste source and the proposed SIF, to determine whichwaste sources are likely to contribute waste volumes to the SIF.

One embodiment utilizes a geographical map with routes highlighted toindicate the possibility of delivering one or more waste types from eachwaste source. In one example, the cumulative distance or time drivenfrom each waste source (red flags on map) is limited by a user-definedvalue (e.g., 50 miles or one hour of transit time) so that a boundedregion in the map can be identified for each waste source. Further,given an assumed fuel cost, the transport cost associated with each legof the journey (along any particular route between the waste source anda given geographical position) can be determined at 106.

Identification of historical waste generation and disposal trends andforecasts of future waste volumes from available data sources at 108.For example, data about wells which have been drilled, wells which areproducing and what they are producing, permits for drilling new wellscan be used to estimate past and future waste generation volumes.Furthermore, data about disposal volumes (and their sources) fromregional disposal sites can be used to model current waste disposalpatterns which influence the economic feasibility of a proposed newsite. Such analysis can be generalized. For example, in residentialwaste, data about new home development or construction permits can beused to forecast new waste disposal capacity needed in a giventerritory. Similar data re: E&P wells can be used to predict futurewaste handling needs.

Identification and analysis of competing disposal facilities and othereconomic drivers impacting expected market share at 110.

Development and Operating costs are estimated at 112 and revenue andexpected waste volume are estimated at 114.

Profitability and margins are calculated based on revenue and costestimations at 116.

Feasibility drivers are identified at 118, including parameters havingdisproportionate effect on feasibility. For example, transport costs dueto a large geographical area may be identified. Once identified, thesedrivers inform potential changes to reach feasibility.

The market area can be defined and analyzed using a network graph 130whose hub is the proposed SIF and whose nodes are the Waste Sources at120. The edges of the graph can be weighted according to expectedvolumes and transport costs (typically measured per unit volume). Oneembodiment of the results of this network graph is in the form of a“heat map” 132 over a given geographical area. In this example, thecolors of the heat map may indicate the historical or forecasted volumesthat have or are expected to be deliverable to any geographicallocation. A variety of functions can be used to visualize (and enableusers to evaluate) different metrics concerning volumes delivered,transportation costs, revenues, overall feasibility, etc.

Given historical waste generation and disposal and expected futureactivity, waste source volumes and locations are used to produce asequence of snapshots in time, at 122. An exemplary “snapshot” indicatesa geographic distribution of volumes of waste types that may beavailable in multiple areas, geographic distributions of potentialincome from the waste volumes, incorporating transport costs, expectedpercentages of market, competing disposal sites, etc.

The resulting economic analysis at 124 indicates potential, economicallyfeasible SIF sites, optimal locations for obtaining the largest volumeof waste, of preferred waste types, at the lowest cost, and at thegreatest profitability.

Further, after performance of the additional FEED studies discussedherein, the results of those studies can be input at 126 and theeconomic feasibility study updated based on whether geologic andoperational feasibility is achieved at identified SIF sites. Thus theidentification of economically acceptable sites is informed byidentification and cross-reference with geologic and operationalfeasibilities. The method ends at 128 once feasibility is indicated. Ifnot, the method returns to an earlier steps, such as step 114, forexample, for further analysis.

Geologic Feasibility

FIG. 3 is a flow chart of an exemplary FEED study, generally designated150, to determine Geologic Feasibility for a proposed SIF according toan aspect of the disclosure.

A method disclosed herein makes use of an Injection PerformanceSimulator (IPS) to simulate matrix injection and hydraulic fracturingcaused by high-pressure injection into subsurface formations. The IPScomprises software executable by a computer and stored in non-transientmemory devices and which use engineering models of subsurface flow anddeformation combined with estimated or known formation properties topredict the effect of changes in one or more FEED parameter on theshort-term and long-term performance of the disposal well. The IPSresults can be used to forecast potential well operations (and costs).

The IPS forecasts the creation of one or more hydraulic fractures causedby fluid injection under pressure. Further, the IPS produces optimalinjection parameters, and in some embodiments well designs, thatmaximize stored waste volumes while assuring containment of the createdhydraulic fractures within the target zone.

An exemplary method begins at step 152. At step 154, necessary data iscollected (and potentially organized, translated, etc.) and/or stored,and input to or made retrievable by the IPS software. The IPS can accessone or more databases of data for existing wells and geologic parametersin the selected area around the proposed SIF. Data can be accessed fromand stored in local or remote, networked or cloud-based databases.Preferably, the method is executed on one or more local or networkedcomputers, operated in series and/or parallel, enabling the operator tocompare, contrast, and study FEED study results from one or multiplewell plans within an accelerated timeframe.

The size of the selected area depends on one or more of the following:governmental regulations, the geologic basin of interest, the extentpotential zones are identifiable and contiguous, and the extent one ormore suitable formations exist for the proposed injection operation anddisposal.

The IPS, in some embodiments, provides analysis on one or multiplephysical “scales,” preferably simultaneously. Namely, analysis isperformed at a regional or “play scale,” where formations and stratahave been characterized by prior geologic studies or where regionalstress and faulting is predetermined. Analysis at a “field scale” isprovided where suitable offset well data (e.g., from neighboring wells,similar wells, etc.). Where offset well data is sufficient, it is usedas a surrogate or proxy for the proposed disposal well. Analysis at the“well scale” uses detailed measurements of the zonal rock propertiesimpacting fluid flow and solids transport (e.g., elastic and mechanicalproperties, permeability, porosity, cementation, natural fractures,etc.) to determine how the geologic zones of interest will respond toslurry injection.

Where offset injection wells exist, the IPS uses their data fromperformed injection operations, such as, daily injection volumes,maximum attained pressures, and other data. Such data can evidence goodor bad “injectability” in a zone or other well-performance metrics asobserved in the injection operations in offset wells.

Rock Property Calculators

In some embodiments, the method performed by the IPS includes performingRock Property Analysis at 156 using rock property calculations based onoffset well data (and/or from the disposal well, if existing). The welldata is used as inputs and the IPS determines rock properties for thegeologic zones of interest.

The Rock Property Analysis requires data including, at a minimum, alithological well log sequence by depth. Additional well logs, includinggamma ray, bulk density, neutron porosity, density porosity,compressional and shear slowness, can be used to increase the accuracyor level of certainty of the output rock properties. Accuracy andreliability obviously impacts the same characteristics of theoptimization results from the IPS method.

The Rock Property Analysis 156 uses a combination of one or more welllogs, Rock Physics Models (RPM), reservoir models, and other geologicdata to automatically generate a geomechanical earth model. Each RockPhysics Model (RPM) consists of one or more functional relationshipswhose arguments include one or more input logs and input parameters andwhose outputs include one or more output logs and output parameters. TheRPM outputs' quality and level of certainty depends on the availabilityand quality of the input logs and input parameters. To increase thequality of the output logs, RPMs may be constructed to depend explicitlyon the observed rock lithology. The quality of the Rock PropertyAnalysis, in turn, also depends on the selection of which RPMs at 160are used and how they are formulated.

In one case, Rock Property Analysis utilizes gamma ray well log readingsalong the measured depth of one or more offset wells to create asurrogate or proxy well log along the trajectory of the proposeddisposal well. While gamma ray is useful to identify and discriminatebetween shale, sand, and shaley-sand intervals, additionalpetro-physical analyses and/or well log types are used in a preferredmethod to determine parameters of zones composed of carbonate,anhydrite, and other lithology. In particular, well logs for the bulkdensity, neutron porosity, density porosity, compressional and shearslowness (inverse sound speeds) are often available and can be used inthe Rock Property Analysis to augment determination of lithology andpetro-physical properties. In addition, references in academic and tradeliterature can be used to clarify Rock Property Analysis.

Lithology Classifier

The IPS Rock Property Analysis, in some embodiments, uses a “lithologyclassifier” at 158 to categorize the lithology for each geologic zonealong a proxy well log. The classifier is a computer-operated algorithmthat uses well logs as input to create a multi-dimensional target space.In a simple form, the target space is a partitioning of atwo-dimensional, cross-plot of the input log types defining thecorresponding axes. The measured data from each of the input logs is aset of realizations derived from measurements taken along a portion ofthe wellbore or from multiple snapshots in time. The realizations fromtwo input logs generate a set of points in this two-dimensional space. Avariety of target spaces can be defined using one or more input logtypes and can be defined in one or more dimensions. Thecomputer-modelled target space is fully “covered,” so no region in thespace is left out of the partitioning.

Each partition is identified with a particular lithology class using aset of empirical or expected rules. For example, empirical data fromneighboring offset wells may indicate that the lithology at the analyzedwell may be well-characterized over a particular range of depths usingcertain combinations of input logs which vary over a fixed range, whichin turn defines a partition. A rule is then used to identify amany-to-one mapping: from the set of realizations of input log values(whose combination sits within the partition) to the previouslydetermined lithology. Once the combinations of input logs are uniquelypartitioned, each measured depth (for example) can be identified with aparticular lithology. The input well logs can also be averaged overvarying lengths so that the equivalent layers' average values can beassigned to a particular lithology. This scenario is useful in the caseof identifying contiguous layers of rock that may be associated with aparticular subsurface formation that is known and or has been previouslyidentified by experts (e.g., the Wilcox group of sands, Eagle Fordshale, etc.).

Additional rules are defined based on measured distance betweenpartitions, which are then used to associate a certain class oflithology with each realization. Training algorithms can be applied toenable the lithology classifier to identify rules following patternsobserved in the input log data for which a subset has previously beenassociated with one or more lithology.

FIG. 4 is an exemplary chart indicating lithology of plotted well logdata points according to an aspect of the disclosure. In this example,the parameters “lambda” and “mu” (characterizing the rock's elasticvolumetric and shear moduli, respectively) are cross-plotted for a groupof rock samples of varying lithology. A separate analysis of the rocksamples was also performed to determine the lithology of each sample.The cross-plot indicates that rock samples with similar lithology tendto be grouped together when certain target spaces are considered asindicated in FIG. 4 by circled clusters for limestone, sandstone, gasand shale lithology. A significant, non-trivial extrapolation of thisresult is applied by the lithology classifier to partition the targetspace and classify one or more combinations of input log values, whichare then considered as representing a the properties of a series of rocksamples along the wellbore.

As another example, compressional sound speed and bulk density well logdata at each point along the proposed well trajectories arecross-plotted in a two-dimensional plot whose axes are “compressionalsound speed” and “bulk density.” Input logs characterize the bulkdensity and compressional sound speed at multiple points (i.e., atmultiple measured depths) along the wellbore. For each measured depth,there is a data point consisting of: (bulk density, compressional soundspeed). The data points are created and the set of points arecross-plotted. Each data point falls into a particular region orpartition that has been pre-identified with a known lithology. Well logscan also be averaged over varying lengths so that the equivalent layers'average values (e.g., of bulk density and compressional sound speed) canbe assigned to a particular lithology.

Once the lithology for the proxy well log has been determined at depthsof interest, the Rock Property Analysis optimally chooses one or moreRPMs to determine the best forecast for the desired rock property thatis possible given the provided input logs. The outputs from one or moreRPMs may be used as the inputs to other RPMs, and this workflow, whichmay include both parallel and series connections between RPMs, isfollowed from the beginning (given the original input logs) to the end(providing a forecast of the desired output logs).

Rock Property and Stress Predictions

The method at 162 provides rock property and stress predictions, in theform of additional well logs, for the targeted disposal zone and atleast one boundary zone. The geomechanical model contains a point-wisedescription of the lithology and rock properties along the proposed welltrajectory, including permeability, porosity, parameters describingleak-off, etc.).

Methods of Reservoir Modelling

The method at 164 leads to an effective, “blocked” (or up-scaled)version of the rock property and stress logs. More complex methods,according to some embodiments, automatically generate two-dimensional orthree-dimensional reservoir computer models at the proposed disposalwell. The reservoir models can be derived directly from the point-wisedescription of the lithology and rock properties along the proposed well(resulting in a “layered” three-dimensional geomechanical model), canutilize geological statistical methods to extrapolate rock propertiesbetween known wells, or can utilize other methods to populate thereservoir model properties. The derived rock properties can includeYoung's modulus, Poisson's ratio, permeability, minimum horizontalstress, and fracture break-down pressure (along with correspondinggradients with respect to depth) along the proposed well.

When using a “layered” three-dimensional model, the geomechanical earthvolume is divided into contiguous, stacked layers. Each layer isassigned geomechanical properties, the values of which are the averageof those previously derived for the proxy well log over the depth ofthat layer. The beginning and ending depths are chosen automatically byan iterative “blocking” algorithm that applies criteria based on thestatistical variability of the well log data over any given interval. Ingeneral, applying the blocking algorithm to each well log separatelyleads to a distinct set of contiguous layers for each well log. Thedifference between these sets is often indicative of the fact thatcertain types of well logs are sensitive to different geology features,and so, are used to highlight potential changes in lithology.Ultimately, a single set of contiguous layers is used to describe thegeologic zones along the proposed well, and all well logs are“re-blocked” on these intervals prior to generating the final modelindicating resulting rock properties.

Blocking and reservoir modelling may require a return to earlier steps,such as 158, depending on performance.

Perforation Intervals, Disposal Capacity

The software identifies perforation intervals 60 within the injectionzone 42 which maximize the amount of disposed waste while stillmaintaining zonal containment. Key to this selection process is also thewell design. This maximum potential disposal is referred to as theDisposal Capacity of the Injection Zones. Some selections of perforationintervals can lead to loss-of-containment and should be avoided. Otherintervals tend to lower the cost of well development while stilloptimizing desired behaviors of fractures created during injection.Multiple perforated intervals (and injection zones) can be utilized,simultaneously or sequentially, to optimize fracturing and disposal.

Injection Simulation

Zones suitable for injection are pre-identified by the operator orautomatically identified by the IPS program. The results of injectinginto particular zones are determined using the geomechanical model(described above) and simulations of slurry transport within thewellbore, slurry entry into the surrounding rock, fracture growth, andreservoir pressure transient behavior (described below). An initial setof injection parameters (e.g., injection flow rate, slurry properties,injection times, etc.), perforation intervals, and well design isselected at 166.

Once a set of potential injection zones and perforation intervals areidentified, the IPS program at 168, in an embodiment of the method,simulates injections of potential injection slurry batches, anddetermines fracture, zonal, and disposal slurry behaviors. The simulatedinjection is performed for a selected set of parameters, namely,selected injection zone(s) adjacent selected boundary zone(s), selectedperforation interval(s), selected injection operational parameters(injection pressures, durations, etc.), and selected waste slurryproperties (density, viscosity, solids characteristics, etc.). Multiplesimulations are run to reach optimal slurry, well, and operationalparameters for maximizing waste disposal while maintaining containment.

For example, the program can simulate a selected, initial BatchInjection wherein the slurry injection begins at zero flow rate, followsa pre-determined injection schedule, and then falls back to zero flowrate for some time period. The simulated injection ceases for a restperiod, during which time fluid pressures in the injection andsurrounding zones decrease as the injected slurry flows from regionsnear the well and fractures to regions further away. Each simulatedBatch Injection is followed by such a rest period according to a BatchDesign provided by the IPS. A series of Batch Cycles is executed by theIPS program.

The initial Batch Injection is followed by subsequent, simulated BatchInjections with modified parameters and/or properties. For example,subsequent batches can be simulated having lower (or no) solid contentrelative to the initial Batch. Subsequent Batches are run with one ormore modified operating parameters (e.g., injection rate, injectionduration, resting duration, slurry viscosity, slurry solidsconcentration, etc.). The plurality of simulations results in acorresponding plurality of simulation results or outputs, namely, zonebehaviors (e.g., fracture growth, permeability changes, increasingminimum horizontal stress, etc.) and a quantified volume of disposedslurry.

System-Level Descriptions to Simulate Reservoir Behaviors

To accommodate a variety of well designs, the IPS can use system-leveldescriptions of the well design, perforated intervals, injection zones,and geomechanical earth model to forecast a variety of reservoir andinjection behaviors. A system-level description of the simulationenvironment refers to the manner in which the separate simulators aretreated, in this case as a combined series and parallel network ofmodular algorithms, each of which may provide feedback to other parts ofthe network. Each module represents a node in the network thatidentifies a set of inputs and outputs, and the IPS contains a varietyof algorithms that satisfy the requirements for each node, even thoughthe algorithms themselves may otherwise produce significantly differentbehavior based on their definition. The modular approach allowson-the-fly replacement of algorithms to test various functionaldependencies in an automated fashion and dictated by the computerprogram itself during the iterative process disclosed herein.

For example, the IPS can use a system-level description of complex fluidtransport, including fluid motion, variable solids concentration,variable fluid rheology, and solid particulate settling and transportalong an arbitrary well trajectory (and well design).

Similarly, the IPS can use a system-level description of partitioning ofcomplex fluids between perforated intervals, leading to distinct,time-varying pressures and flow rates at each perforated interval.

Similarly, the IPS can use a system-level description of Passage ofComplex Fluids through the Perforated Intervals into the reservoirthrough open-hole or mechanical completions modeled by one or morefunctional relationships between fluid pressure and flow rate.

Similarly, the IPS can use a system-level description of Flow of ComplexFluid and possible fracture growth (and fluid leak-off from inside thefracture) into one or more of the surrounding formations both inside andoutside the Injection Zone.

Similarly, the IPS can use a system-level description of time-varyingcommingling between, or isolation of, injected fluid between multipleInjection Zones, and, in particular, the Disposal Capacity for thecombined Injection Zones (which can include permeable strata betweenthem) can or cannot be larger than the Disposal Capacity of theInjection Zones considered separately.

Similarly, the IPS can use a system-level description of time-varyingstress interference between one or more fractures emanating frommultiple Perforated Intervals or between an injection-induced fractureand a natural fracture.

Similarly, the IPS can use a system-level description of the transitionbetween matrix, diffusion-like flow, to fracture creation and growthonce local pore pressure reaches formation break-down or fracturepropagation pressure, and the impact of transient injection andreservoir behavior on the mode of injection and partitioning of fluidbetween Perforated Intervals.

Identification of Feasible Zones, Structural Features, Containment LossScenarios

There are three key structural features that characterize thesuitability of injecting slurry into an injection zone, and these areoften distinct from suitable zonal features (e.g., as desired for waterdisposal injection, enhanced oil recovery, or pressure maintenance). Themethod, in some embodiments, identifies three loss-of-containmentscenarios, the possibility of which have significant impact on slurryinjection feasibility. The scenarios are characterized by differingminimum horizontal stress, permeability, and rock stiffness of theboundary layer above the injection zone (and sometimes of the boundarylayer below). The described scenarios are not exhaustive but arecommonly identified in operational, environmental, or regulatory issuessince slurry in the injection zone can pass to shallower depths.

A first containment scenario is Fracture Containment due to overlyingzonal layer pressure constraint. In high-pressure injection operations,where waste slurry (or other fluid) is injected into the injection zone,the overlying zonal layers act to constrain pressure-induced fracturegrowth within the injection zone. The software program predicts fracturegrowth using the predicted minimum horizontal stress of the boundarylayer and determines a minimum pressure in the injection zone below theupper boundary zone resulting in extending fractures into the boundaryzone. Alternately, the program identifies a maximum pressure which canbe generated in the injection zone below the upper boundary zone withoutcausing breach of containment by creating fractures extending into theupper boundary zone.

A second containment scenario to consider is Fluid Containment due torelative permeability of overlying zonal layers compared to thatobserved in the injection zone. The IPS utilizes the permeability in thezonal layers and injection zone to determine how quickly the fluidpressure generated during injection is relieved, whether this occurs faraway from the well and or above or below the injection zone. In caseswhere the overlying zone rock is much less permeable than the injectionzone, the injected slurry is less likely to breach containment andmigrate outside the injection zone during conditions of matrix injection(i.e., when no fracture is created). Fractured injection is different,however, in that Fluid Containment is expected to occur (or at leastcontribute to the overall likelihood of containment) when thepermeability in the overlying zonal layers are much larger than thepermeability in the injection zone (e.g., when this ratio is a factor often or more). In that case, fracture(s) in the injection zone maypropagate up to the overlying zone but then cease upward propagation asthe fluid pressure inside the fracture(s) is released relatively quicklyinto the overlying zone.

A third containment scenario to consider if Stiffness Containment.Stiffness containment occurs where the overlying zone rock is of higherstiffness than the injection zone. In such a case, fractures createdduring slurry injection are more likely to remain pinned to the zonejust below the containing layer since application of additional and orcontinue fluid pressure within the injection zone will more likely causefracture length growth (below the boundary layer) rather than fracturewidth growth (into the boundary layer). Although the actual fractureorientation is governed by the orientation of the principal stresses inthe rock (as well as other reservoir properties and features ofpropagation) and so may be in any direction, typical subsurface stressesare oriented such that fracture height grows in the vertical direction,whereas fracture width and length are aligned in the horizontaldirections.

The method can further include at 170 a feedback loop and iterativecalculation to continually optimize the available parameters (e.g.,injection flow rate, slurry viscosity, slurry density, etc.) in order toprevent these loss of containment scenarios from occurring.

Disposal Capacity, Domain Capacity Calculations

The Disposal Capacity for a waste injection well is determined by theIPS at 172. In an embodiment, the IPS uses the geomechanical earth modeland injection simulation results to predict principal stress increaseswithin the zones during the disposal process. Each Batch Cycle depositsa certain volume of particulate solids that remain inside one or morecreated fractures after each cycle. This change in volume causes achange (typically an increase) in the overall stress within theinjection zone and boundary zones formations above and below it.

The stress containment scenario outlined above is achieved when thedifference between the minimum horizontal stress in the containing zoneis larger than the stress in the zone below it. The stiffnesscontainment scenario also lends itself to effective measure of requiredstress to propagate a slurry-laden fracture through the containinglayer.

The IPS provides techniques for determining how subsequent Batch Cyclescan increase the minimum horizontal stress below the containment layerand how many Batch Cycles (and of what volume and characteristics) canbe injected before the minimum horizontal stress in the containmentlayer no longer prevents loss of containment. This number is referred toas the Total Number of Batches. The IPS program forecasts injection intoone or more injection zones, simultaneously or sequentially, andprovides a direct estimate of the contribution of each injection zone tooverall Disposal Capacity. The method can include a feedback loop toautomatically (or upon request) update the Batch Design and selection ofInjection Zones and Perforated Intervals to maximize Disposal Capacitybased on the IPS results from multiple Batch Designs.

The resulting outputs are mathematically compared at 174, preferably asthey are completed, with outputs of other simulations as part of findingan optimum Batch Design which maximizes selected outputs (e.g., disposalcapacity). Such comparisons inform selection of parameters for futuresimulations, resulting in iterative simulations approaching an optimalBatch Design. Iterative and convergence algorithms are known in the artand can be applied to reduce calculation times, limit necessarysimulation runs, etc. Convergence algorithms can take various forms, forexample, evolutionary algorithms, genetic algorithms, meta-heuristicoptimization algorithms, trial-and-error, linear and non-linearoptimization techniques, least squares regression, etc. In case nosatisfactory solution is obtained (e.g., when none of the batch designsensures containment), none of the results are considered as potentialcandidates and none is therefore optimum. In case one or more solutionsobtained are indeed satisfactory, these solutions are carried forward inthe FEED with their relative disposal capacities and evaluated inconcert with the other FEED criteria to determine the overall optimumbatch design to use in practice.

The iterative simulation approach requires large numbers of simulationsand corresponding processing power to be time-effective. The simulationscan, in an embodiment, be computed using parallel computing. Parallelcomputing allows the software to calculate how varied Batch Designsaffect Disposal Capacity so optimized operational parameters areprovided in an accelerated timeframe. Where real-time data is used,parallel computing can be key in running sufficient simulations andcomparing their impacts quickly enough to allow newly-optimizedparameters to be executed.

The geologic feasibility ends at block 176 or is repeated in whole orpart if feasibility is not achieved.

Operational Feasibility

FIG. 5 is a flow chart of an exemplary FEED study, generally designated200, to determine Geologic Feasibility for a proposed SIF according toan aspect of the disclosure.

The exemplary method, starting at 202, illustrated in FIG. 5 indicatesthat Operational Feasibility considers at least three componentsimpacting operational optimization: Facility Design 204, Well Design206, and Well Management 208. Each component impacts the proposed SlurryInjection Facility's maximum Disposal Capacity. Consequently, each mustbe considered simultaneously as part of an iterative process in concertwith the design constraints identified by the Geologic Feasibility(described above).

Facility Design 204 constraints ensure that the surface equipmentprovide enough throughput and storage of the various waste streams toaccommodate the proposed Batch Designs and forecast economic conditions.In particular, as variable types and amounts of waste arrive at thesite, the method considers volumes to be processed and stored during thetimeframes of a Batch Injection and subsequent Rest Period.

Well Design 206 constraints ensure that the selection of proposed wellcompletion and trajectory optimally target one or more injection zonestargeted earlier in the IPS program method, namely at the optimizationloop indicated at 166 in FIG. 3. In particular, the IPS programforecasts injection-induced fracturing which may interfere withneighboring wells. Similarly, the Well Design can be updated by the IPSprogram's selection of Perforated Intervals to control and or minimizethe risk of loss-of-containment. A set of geologic constraints isapplied at 208, with an internal iterative loop is connected back to theFacility Design and Well Design, to ensure consistency with GeologicFeasibility 166.

Well Management System constraints at 210 are utilized such that, oncethe well is constructed and operational, the program optimizes DisposalCapacity with respect to parameters that can be controlled by operatorsat the facility site. The feasibility study ends at block 212.

Well Management System

FIG. 6 is a flow chart of an exemplary computerized Well ManagementSystem (WMS) method, generally designated 300, to actively (and in someembodiments automatically) manage an operating waste injection well andwaste disposal facility. The WMS method begins at 302 and depends onexisting facility, well and operation designs, which can be providedfrom the optimized results from previously performed Facility Design204, Well Design 206, Injection Parameters and Intervals determination166, and Selected Batch Designs at 174. For clarity, the method isdiscussed in relation to a disposal injection operation.

In an exemplary WMS method, a disposal injection operation is begun 304,at the well site, according to an initial Batch Injection Scheduleselected and predicted to maintain zonal containment of the injectedslurry and/or optimize disposal volume. The operation is performedaccording to planned operating parameters (e.g., injection pressure,injection pump rates, injection and resting periods, etc.) into atargeted disposal zone of a reservoir which is modelled (e.g.,lithography, known fractures, stresses, multi-dimensional space, etc.)in a control computer system. The model reservoir can be taken from thegeologic feasibility study explained above.

The well site includes sensor equipment and operational equipmentnetworked to the control system and having controllers, gateways, etc.,as described herein above. Smart sensors are utilized to insure accurateand timely communication of real time measured data to the computercontrol system via the network.

A suite of real time measured data 306, from the sensor equipment, areinput to an injection performance simulator program (such as the IPSprogram described above). The “live” measured data is communicated tothe simulator software and simulations are run at 310, resulting inforecast operation results based on the real time data. The IPS programalso utilizes other information, such as archived measured data, acomputer-modelled reservoir corresponding to the targeted reservoir, acomputer-modelled well corresponding to the physical well, and/or acomputer-modelled injection schedule corresponding to the BatchInjection Schedule being implemented at the well. The simulations arerun sequentially, during well operations (in “real time”) withsubsequent simulations updated with more recent real time measured data.As described elsewhere herein, the method is preferably performed usingparallel computer processors to facilitate speed of simulations andother calculations.

The simulation results are monitored for selected, predicted reservoirbehaviors at 314 and can be compared by the program to previouslypredicted results using the initial injection operation schedule.Significant deviations from the initial injection schedule predictionsare identified and quantified. For example, simulated reservoirbehavior, using the real time data, might predict a potential loss ofcontainment. That is, slurry or fractures migrating to containment orboundary zones. Further, simulations can be monitored for indicationsthat a greater disposal volume than initially planned can be achievedduring the injection operation. Increased potential volume disposal canresult from unanticipated fractures, accelerated slurry dispersal in thezone, etc.

Real-time, near real-time, and the like, as used herein, means of orrelating to a system in which data is processed and/or communicatedwithin fractions of a second so that it is available virtuallyimmediately, or of or relating to a data processing system in which acomputer receives constantly changing data (such as measured data duringan injection procedure) and processes the data sufficiently rapidly tobe able to control the implicated equipment (pumps, mixers, valves,etc.).

As described, the IPS program uses the input measured data to runinjection operation simulations based on the known and estimatedformation parameters such as, zonal rock properties, lithography, zonalstresses, and modelled reservoir, well, and injection simulations, etc.,(if known). The Batch Injection is planned to be performed according toan initial Injection Schedule which sets out operational parameters suchas injection flow rates, slurry viscosity, slurry density, injectiontimes, rest times, etc. Often the acceptable injection parameters aresupplied in operational ranges. In an embodiment, an initial InjectionSchedule is a selected, optimal injection operation resulting from themethods described herein using the IPS program.

Controllable and non-controllable parameters are used by the IPSprogram. For example, controllable parameters are controllable by theoperator during the injection operation, such as injection flow rates,slurry viscosity, slurry density, injection times, rest times, etc.Similarly, non-controllable parameters are identified (e.g., MASIP,regulatory constraints, formation rock strength, etc.) and one or morereal-time, measurable data metrics are defined. In an embodiment, theIPS program has input identifying specific operating parameters andmetrics that are critical to well performance and to which well behavioris sensitive. These operating parameters are likely candidates foron-the-fly changes during the injection operation since such changes aremost likely to provoke meaningful changes in expected well behavior.

An exemplary real time, measurable data metric is an Operating Window,which is defined as the difference between the well MASIP and thedynamic, well-head pressure obtained while pumping at the maximum,scheduled (design) rate. The Operating Window changes over time and inparticular during each Batch Cycle. Given that injection must cease whenthe Operating Window decreases to zero, well management is optimized,according to the IPS program outputs, to ensure that the OperatingWindow provides a sufficient margin to accommodate the desired disposalvolume during the Batch Cycle.

The WMS method utilizes the IPS program to incorporate real-timemeasured data from the site to predict and respond to changes inidentified critical data and simulation output metrics, such as theOperating Window. The IPS program runs updated injection simulationsbased on the measured real time data. The resulting outputs can identifypotential unacceptable outcomes (e.g., loss of containment) andpotential improvements to the scheduled injection operation (e.g., anoperational parameter change which will result in greater injected wastevolume) based on allowed ranges of the measurable data metrics, outputresults, etc.

Where a selected simulation result occurs (e.g., loss of containment,opportunity for increased disposal volume, etc.), the WMS method or IPSprogram determines, using iterative simulations, an updated, optimizedoutcome achieved by an updated, optimized injection schedule at 316.(Determination of optimal results is addressed elsewhere herein.) TheIPS program identifies and quantifies operational parameter changes tomake to the initial or currently-running injection operation to achievethe updated injection schedule at 318. The IPS program, to avoid apredicted containment breach, based on simulations using the initialinjection schedule updated by real time measured data, calculates one ormore changes to make to one or more operational parameters. Theparameter changes (e.g., injection rate, pump speed, etc.) correlate tothe updated, optimized injection schedule. For example, the IPS programcan indicate a new (higher or lower) injection rate (or a proposedchange from the initial or current rate).

The IPS program, in an embodiment, provides one or more recommendationsto the operator to modify the controllable parameters according to theupdated and changed operating parameters and schedule. The operator canthen perform tasks to achieve the recommended operational parameter(e.g., slowing injection rate), or, in an alternate embodiment, theoperator can receive the change recommendation and either authorize ordeny the change at 320. Upon authorization, the WMS programautomatically operates injection equipment (e.g., pumps, materialhandling equipment, valving, etc.) to effect the recommended andauthorized change at 322. For example, the WMS program, via the network,communicates to the in-use injection pump, providing instructions to thepump controller to reduce (or increase) the pump rate to a given rate orrange. That is, the WMS communicates new parameters to the appropriateequipment, which the equipment performs to effect a real time (or nearreal time) change in equipment settings or operation. Thus, the WMSprogram and networked system provide on-the-fly change to operationequipment and parameters, automatically.

Changes can be verified by the WMS and IPS programs using updated realtime measured data. After the recommended changes are made to thecontrollable operational parameters, injection operations continue, backto 304, and the method is performed again, preferably until completionof the initial or updated injection schedule.

The WMS method can also utilize constraints on Controllable Parameterssuch that the WMS cannot recommend any parameter changes outside ofthose constraints. This effectively acts as a check on the WMS methodand IPS program to prevent wild fluctuation of injection parametersduring a Batch Cycle.

In another embodiment, the WMS method can utilize historical behavior ofthe well (particularly historical data from prior-run Batches),real-time measured from the well site, and planned future Batch Cyclesto maximize overall Disposal Capacity of the well across multiple BatchInjections. For example, if a forecast well behavior, based on real timedata during an on-going Batch Cycle, indicates permanent damage to thewell or reservoir (negatively effecting overall disposal volume), theWMS program can recommend a change in the current Batch Cycle injectionschedule to limit or eliminate the potential damage.

Exemplary optimum scenarios are discussed here. Data obtained using theWMS method is included in the IPS to determine, automatically or withoperator input, how best to achieve the following identified optimumscenarios.

In case the IPS recommends a single fracture be utilized for disposal,the operator or program manages the Controllable Parameters to createthe single fracture using one Perforated Interval, and preferably re-usethat fracture during subsequent Batch Injections.

In case the IPS recommends a sequence of fractures be utilized fordisposal, the operator or program manages the Controllable Parameters tocreate one or more fractures along one Perforated Interval. The created,multiple fractures are preferably re-entered by injected slurry infuture Batch Injections. Alternately, one or more new fractures can becreated during subsequent Batch Injections.

In case the IPS recommends multiple fractures be simultaneously used fordisposal, the operator or program includes multiple Perforated Intervalsin the Well Design, and manages the Controllable Parameters so flow intoeach Perforated Interval is balanced according to an optimal BatchDesign. The created, multiple fractures can be preferably re-used in oneor more subsequent Batch Injections. Alternately, new fractures can becreated during subsequent Batch Injections.

To ensure optimization is achieved, the impacts of both actual andsimulated Batch Injections are evaluated concurrently with the welloperation. Batch Design solutions from the IPS are adopted whichforecast desirable reservoir behavior, and data-based metrics from theWell Management System are used to continually update the IPS andconfirm the predicted behavior.

Exemplary Determined Parameters

Certain embodiments of the methods and systems disclosed herein havebeen performed to evaluate the methods and systems. In particular, themethods have been applied to thirty-four geologic formations in variousbasins across the United States. In all of these cases, the optimizedBatch Injection design consists of an injection rate between 1 and 20Bbls/min (bpm), injection duration between 1 and 144 hours (hrs), slurryviscosity between 1 and 35 centipoise (cP), solids concentration between0 and 35%, with solids particle size in the slurry of less than 400microns; the optimized Rest Period is between 1 and 144 hrs; and theoptimized MASIP is defined so that the bottom-hole pressure (BHP) at theoptimal injection rate is above the fracture break-down pressure, whichis often observed to have a gradient of between 0.3-1.0 psi/ft at thedepth of given Injection Zones. More preferable ranges for theseparameters can be forecast by the IPS program to provide maximizedDisposal Capacities for the particular formations of interest.

Where the target geological formation is the Wilcox formation, theoptimized Batch Injection design consists of injection rate preferablybetween 3 and 12 bpm, injection duration preferably between 8 and 24hrs, slurry viscosity preferably between 10 and 30 centipoise (cP),solids concentration preferably between 5 and 20%, and Rest Periodpreferably between 3 and 18 hrs.

Where the target formation is the Edwards formation, the optimized BatchInjection design consists of injection rate preferably between 4 and 8bpm, injection duration preferably between 6 and 24 hrs, slurryviscosity preferably between 15 and 30 cP, solids concentrationpreferably between 12 and 20%, and Rest Period preferably between 6 and24 hrs.

Where the target formation is the Delaware Basin formation, theoptimized Batch Injection design consists of injection rate preferablybetween 3 and 10 bpm, injection duration preferably between 6 and 28hrs, slurry viscosity preferably between 20 and 30 cP, solidsconcentration preferably between 8 and 15%, and Rest Period preferablybetween 4 and 24 hrs.

Where the target formation is the Wolfcamp formation, the optimizedBatch Injection design consists of injection rate preferably between 3and 12 bpm, injection duration preferably between 4 and 28 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 12 and 20%, and Rest Period preferably between 6 and36 hrs.

Where the target geological formation is the Minnelusa formation, theoptimized Batch Injection design consists of injection rate preferablybetween 4 and 9 bpm, injection duration preferably between 6 and 24 hrs,slurry viscosity preferably between 15 and 30 centipoise, solidsconcentration preferably between 2 and 15%, and Rest Period preferablybetween 6 and 18 hrs.

Where the target geological formation is the Dakota Sands formation, theoptimized Batch Injection design consists of injection rate preferablybetween 4 and 15 bpm, injection duration preferably between 3 and 24hrs, slurry viscosity preferably between 15 and 30 centipoise, solidsconcentration preferably between 5 and 20%, and Rest Period preferablybetween 6 and 36 hrs.

Where the target formation is the Mission Canyon formation, theoptimized Batch Injection design consists of injection rate preferablybetween 6 and 12 bpm, injection duration preferably between 6 and 26hrs, slurry viscosity preferably between 20 and 30 cP, solidsconcentration preferably between 5 and 15%, and Rest Period preferablybetween 3 and 18 hrs.

Where the target formation is the Arbuckle formation, the optimizedBatch Injection design consists of injection rate preferably between 4and 14 bpm, injection duration preferably between 6 and 18 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 5 and 20%, and Rest Period preferably between 4 and24 hrs.

Where the target formation is the Ellenburger formation, the optimizedBatch Injection design consists of an injection rate between 4 and 12bpm, injection duration preferably between 6 and 24 hrs, slurryviscosity preferably between 15 and 30 cP, solids concentrationpreferably between 12 and 20%, and Rest Period preferably between 16 and48 hrs.

Where the target formation is the Clear Fork formation, the optimizedBatch Injection design consists of injection rate preferably between 3and 10 bpm, injection duration preferably between 3 and 24 hrs, slurryviscosity preferably between 10 and 30 cP, solids concentrationpreferably between 8 and 25%, and Rest Period preferably between 4 and72 hrs.

Where the target formation is the Rustler formation, the optimized BatchInjection design consists of injection rate preferably between 6 and 12bpm, injection duration preferably between 12 and 38 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 5 and 20%, and Rest Period preferably between 12 and28 hrs.

Where the target formation is the Sparta formation, the optimized BatchInjection design consists of injection rate preferably between 4 and 11bpm, injection duration preferably between 6 and 48 hrs, slurryviscosity preferably between 10 and 30 cP, solids concentrationpreferably between 8 and 22%, and Rest Period preferably between 6 and18 hrs.

Where the target formation is the Woodbine formation, the optimizedBatch Injection design consists of injection rate preferably between 2and 10 bpm, injection duration preferably between 2 and 48 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 4 and 20%, and Rest Period preferably between 12 and48 hrs.

Where the target formation is the Frio formation, the optimized BatchInjection design consists of injection rate preferably between 4 and 15bpm, injection duration preferably between 3 and 48 hrs, slurryviscosity preferably between 10 and 30 cP, solids concentrationpreferably between 12 and 30%, and Rest Period preferably between 6 and48 hrs.

Where the target formation is the Clayton formation, the optimized BatchInjection design consists of injection rate preferably between 6 and 12bpm, injection duration preferably between 2 and 24 hrs, slurryviscosity preferably between 15 and 30 cP, solids concentrationpreferably between 5 and 20%, and Rest Period preferably between 6 and18 hrs.

Where the target formation is the Corral Creek formation, the optimizedBatch Injection design consists of injection rate preferably between 3and 9 bpm, injection duration preferably between 6 and 24 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 12 and 20%, and Rest Period preferably between 12 and48 hrs.

Where the target formation is the Hawkeye formation, the optimized BatchInjection design consists of injection rate preferably between 5 and 12bpm, injection duration preferably between 7 and 36 hrs, slurryviscosity preferably between 10 and 30 cP, solids concentrationpreferably between 12 and 22%, and Rest Period preferably between 12 and38 hrs.

Where the target formation is the Bailey formation, the optimized BatchInjection design consists of injection rate preferably between 3 and 12bpm, injection duration preferably between 6 and 24 hrs, slurryviscosity preferably between 15 and 30 cP, solids concentrationpreferably between 8 and 18%, and Rest Period preferably between 4 and72 hrs.

Where the target formation is the Mt. Simon formation, the optimizedBatch Injection design consists of injection rate preferably between 5and 15 bpm, injection duration preferably between 4 and 30 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 12 and 20%, and Rest Period preferably between 6 and72 hrs.

Where the target formation is the Devonian formation, the optimizedBatch Injection design consists of injection rate preferably between 3and 11 bpm, injection duration preferably between 6 and 24 hrs, slurryviscosity preferably between 10 and 30 cP, solids concentrationpreferably between 5 and 20%, and Rest Period preferably between 4 and48 hrs.

Where the target geological formation is the Bone Spring formation, theoptimized Batch Injection design consists of injection rate preferablybetween 6 and 12 bpm, injection duration preferably between 2 and 40hrs, slurry viscosity preferably between 15 and 30 centipoise, solidsconcentration preferably between 5 and 25%, and Rest Period preferablybetween 6 and 18 hrs.

Where the target formation is the Newburg formation, the optimized BatchInjection design consists of injection rate preferably between 5 and 12bpm, injection duration preferably between 3 and 32 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 5 and 20%, and Rest Period preferably between 4 and24 hrs.

Where the target formation is the Queen formation, the optimized BatchInjection design consists of injection rate preferably between 3 and 11bpm, injection duration preferably between 3 and 24 hrs, slurryviscosity preferably between 10 and 30 cP, solids concentrationpreferably between 8 and 22%, and Rest Period preferably between 6 and18 hrs.

Where the target geological formation is the San Andreas formation, theoptimized Batch Injection design consists of injection rate preferablybetween 4 and 15 bpm, injection duration preferably between 6 and 24hrs, slurry viscosity preferably between 15 and 30 centipoise, solidsconcentration preferably between 12 and 20%, and Rest Period preferablybetween 6 and 48 hrs.

Where the target formation is the Yeso formation, the optimized BatchInjection design consists of injection rate preferably between 3 and 12bpm, injection duration preferably between 5 and 38 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 5 and 25%, and Rest Period preferably between 3 and18 hrs.

Where the target formation is the Ohio Shale formation, the optimizedBatch Injection design consists of injection rate preferably between 6and 12 bpm, injection duration preferably between 6 and 24 hrs, slurryviscosity preferably between 10 and 30 cP, solids concentrationpreferably between 5 and 20%, and Rest Period preferably between 4 and18 hrs.

Where the target formation is the Trempealeau Dolomite formation, theoptimized Batch Injection design consists of injection rate preferablybetween 5 and 10 bpm, injection duration preferably between 4 and 32hrs, slurry viscosity preferably between 5 and 30 cP, solidsconcentration preferably between 12 and 25%, and Rest Period preferablybetween 6 and 26 hrs.

Where the target formation is the Big Injun formation, the optimizedBatch Injection design consists of injection rate preferably between 3and 9 bpm, injection duration preferably between 3 and 18 hrs, slurryviscosity preferably between 5 and 30 cP, solids concentrationpreferably between 5 and 20%, and Rest Period preferably between 6 and24 hrs.

Where the target formation is the Geneso formation, the optimized BatchInjection design consists of injection rate preferably between 2 and 12bpm, injection duration preferably between 6 and 24 hrs, slurryviscosity preferably between 10 and 30 cP, solids concentrationpreferably between 5 and 22%, and Rest Period preferably between 6 and40 hrs.

Where the target formation is the Weir formation, the optimized BatchInjection design consists of injection rate preferably between 3 and 12bpm, injection duration preferably between 8 and 52 hrs, slurryviscosity preferably between 15 and 30 cP, solids concentrationpreferably between 10 and 25%, and Rest Period preferably between 6 and28 hrs.

Where the target formation is the Knox formation, the optimized BatchInjection design consists of injection rate preferably between 6 and 12bpm, injection duration preferably between 4 and 24 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 4 and 20%, and Rest Period preferably between 6 and32 hrs.

Where the target formation is the Cliffton Forge/Medina formation, theoptimized Batch Injection design consists of injection rate preferablybetween 4 and 12 bpm, injection duration preferably between 8 and 24hrs, slurry viscosity preferably between 10 and 30 centipoise, solidsconcentration preferably between 8 and 20%, and Rest Period preferablybetween 4 and 18 hrs.

Where the target formation is the Gordon formation, the optimized BatchInjection design consists of injection rate preferably between 3 and 11bpm, injection duration preferably between 6 and 28 hrs, slurryviscosity preferably between 20 and 30 cP, solids concentrationpreferably between 5 and 23%, and Rest Period preferably between 8 and18 hrs.

Where the target formation is the Oriskany formation, the optimizedBatch Injection design consists of injection rate preferably between 3and 12 bpm, injection duration preferably between 12 and 72 hrs, slurryviscosity preferably between 10 and 30 cP, solids concentrationpreferably between 12 and 20%, and Rest Period preferably between 2 and24 hrs.

Methods

The disclosure is provided in support of the methods claimed or whichmay be later claimed. Specifically, this support is provided to meet thetechnical, procedural, or substantive requirements of certain examiningoffices. It is expressly understood that the portions or actions of themethods can be performed in any order, unless specified or otherwisenecessary, that each portion of the method can be repeated, performed inorders other than those presented, that additional actions can beperformed between the enumerated actions, and that, unless statedotherwise, actions can be omitted or moved. Those of skill in the artwill recognize the various possible combinations and permutations ofactions performable in the methods disclosed herein without an explicitlisting of every possible such combination or permutation. It isexplicitly disclosed and understood that the actions disclosed, bothherein below and throughout, can be performed in any order (xyz, xzy,yxz, yzx, etc.) without the wasteful and tedious inclusion of writingout every such order.

Computerized Systems and Components

Computer and Computerized Systems. The systems, methods, and otherembodiments according to the disclosure include computerized systemsrequiring the performance of one or more methods or steps performed onor in association with one or more computer.

The term computer as used herein and in the claims is not and is notintended to be a means-plus-function term or element. A computer is aprogrammable machine having two principal characteristics, namely, itresponds to a set of instructions in a well-defined manner and canexecute a pre-recorded list of instructions (e.g., a program). Acomputer according to the present disclosure is a device with aprocessor and a memory. For purposes of this disclosure, a computerincludes a server, a personal computer, (i.e., desktop computer, laptopcomputer, netbook), a mobile communications device, such as a mobile“smart” phone, and devices providing functionality through internalcomponents or connection to an external computer, server, or globalcommunications network (such as the internet) to take direction from orengage in processes which are then delivered to other system components.

Those of skill in the art recognize that other devices, alone or inconjunction with an architecture associated with a system, can provide acomputerized environment for carrying out the methods disclosed herein.The method aspects of the disclosure are computer implemented and, moreparticularly, at least one step is carried out using a computer.

General-purpose computers include hardware components. A memory ormemory device enables a computer to store data and programs. Commonstorage devices include disk drives, tape drives, thumb drives, andothers known in the art. An input device can be a keyboard, mouse,hand-held controller, remote controller, a touchscreen, and other inputdevices known in the art. The input device is the conduit through whichdata and instructions enter a computer. An output device is a displayscreen, printer, or other device letting the user sense what thecomputer has accomplished, is accomplishing, or is expected toaccomplish. A central processing unit (CPU) is the “brains” of thecomputer and executes instructions and performs calculations. Forexample, typical components of a CPU are an arithmetic logic unit (ALU),which performs arithmetic and logical operations and a control unit (CU)which extracts instructions from memory, decodes and executes them,calling on the ALU when necessary. The CPU can be a micro-processor,processor, one or more printed circuit boards (PCBs). In addition tothese components, others make it possible for computer components towork together or in conjunction with external devices and systems, forexample, a bus to transmit data within the computer, ports forconnectivity to external devices or data transmission systems (such asthe internet), wireless transmitters, read and read-write devices, etc.,such as are known in the art.

A server is a computer or device on a network that manages networkresources. There are many different types of servers, including remote,live and network access servers, data servers, member servers, stagingservers, etc. A server can be hardware and/or software that managesaccess to a centralized resource or service in a network. For purposesof this disclosure, the term “server” also includes “virtual servers”which can be hosted on actual servers.

A computer network or data network is a communications network allowingcomputers to exchange data, with networked devices passing data to eachother on data connections. Network devices that originate, route, andterminate data are called nodes. The connections (links) between nodesare established using wire or wireless media. Nodes can include hosts,such as PCs, phones, servers, and networking hardware. Devices arenetworked together when one device is able to exchange information withthe other device whether or not they have a direct connection to eachother. Computer networks support applications such as access to theWorld Wide Web (WWW) or internet, shared use of application and storageservers, printers, and use of email and instant messaging applications.Computer networks differ in the physical media to transmit signals,protocols to organize network traffic, network size, topology, andorganizational intent.

A (control) gateway is a network node that acts as an entrance toanother network. In homes, the gateway is the ISP (internet serviceprovider) that connects the user to the internet. In enterprises, thegateway node often acts as proxy server and firewall. The gateway isalso associated with a router, which uses headers and forwarding tablesto determine where packets are sent, and a switch, which provides theactual path for the packet in and out of the gateway.

A (control) gateway for the particular purpose of connection toidentified cloud storage, often called a cloud storage gateway, is ahardware-based and/or software-based appliance located on the customerpremises that serves as a bridge between local applications and remotecloud-based storage and are sometimes called cloud storage appliances orcontrollers. A cloud storage gateway provides protocol translation andconnectivity to allow incompatible technologies to communicatetransparently. The gateway can make cloud storage appear to be an NAS(network attached storage) filer, a block storage array, a backuptarget, a server, or an extension of the application itself. Localstorage can be used as a cache for improved performance. Cloud gatewayproduct features include encryption technology to safeguard data,compression, de-duplication, WAN optimization for faster performance,snapshots, version control, and data protection.

A “bridge” connects two (local) networks, often connecting a localnetwork using an internet router.

A router forwards data packets along networks and is connected to atleast two networks, commonly two LANs, WANs, or a LAN and its ISP'snetwork. Routers are located at “gateways,” the places where two or morenetworks connect. Routers use headers and forwarding tables to determinepaths for forwarding packets and use protocols to communicate with eachother to configure a route between hosts.

The disclosure includes one or more databases for storing informationrelating to aspects of the disclosure. The information stored on adatabase can, for example, be related to a private subscriber, a contentprovider, a host, a security provider, etc. One of ordinary skill in theart appreciates that “a database” can be a plurality of databases, eachof which can be linked to one another, accessible by a user via a userinterface, stored on a computer readable medium or a memory of acomputer (e.g., PC, server, etc.), and accessed by users via globalcommunications networks (e.g., the internet) which may be linked usingsatellites, wired technologies, or wireless technologies.

Data services can include applications for data processing, querying,and manipulation. For example, a Structured Query Language (SQL) orcommercially available APACHE (trade name) HADOOP (trade name) can beused. Data storage and services can be performed on-site or remotely,accessible via network, and allow a user to access, manage, upload anddownload data, and query the databases and data services as needed.

In computer networking, “cloud computing” is used to describe a varietyof concepts involving a large number of computers connected through anetwork (e.g., the Internet). The phrase is often used in reference tonetwork-based services, which appear to be provided by real serverhardware, but which are in fact served by virtual hardware, simulated bysoftware running on one or more machines. Virtual servers do notphysically exist and can therefore be moved around, scaled up or down,etc., without affecting the user.

In common usage, “the cloud” is essentially a metaphor for the internet.“In the cloud” also refers to software, platforms, and infrastructuresold “as a service” (i.e., remotely through the internet). The supplierhas actual servers which host products and services from a remotelocation, so that individual users do not require servers of their own.End-users can simply log-on to the network, often without installinganything, and access software, platforms, etc. Models of cloud computingservice are known as software as a service, platform as a service, andinfrastructure as a service. Cloud services may be offered in a public,private, or hybrid networks. Google, Amazon, Oracle Cloud, and MicrosoftAzure are well-known cloud vendors.

Software as a service (SaaS) is a software delivery model in whichsoftware and associated data are centrally hosted on the Cloud. UnderSaaS, a software provider licenses a software application to clients foruse as a service on demand, e.g., through a subscription, timesubscription, etc. SaaS allows the provider to develop, host, andoperate a software application for use by clients who just need acomputer with internet access to download and run the softwareapplication and/or to access a host to run the software application. Thesoftware application can be licensed to a single user or a group ofusers, and each user may have many clients and/or client sessions.

Typically, SaaS systems are hosted in datacenters whose infrastructureprovides a set of resources and application services to a set ofmultiple tenants. A “tenant” can refer to a distinct user or group ofusers having a service contract with the provider to support a specificservice. Most SaaS solutions use a multi-tenant architecture where asingle version of the application, having a single configuration (i.e.,hardware, operating system, and network) is used by all tenants(customers). The application can be scaled by installation on severalmachines. Other solutions can be used, such as virtualization, to managelarge numbers of customers. SaaS supports customization in that theapplication provides defined configuration options allowing eachcustomer to alter their configuration parameters and options to choosefunctionality and “look and feel.”

SaaS services are supplied by independent software vendors (ISVs) orApplication Service Providers (ASPs). SaaS is a common delivery modelfor business applications (e.g., office and messaging, management, anddevelopment software, and for accounting, collaboration, managementinformation systems (MIS), invoicing, and content management.

SaaS is an advantage to end-users in that they do not need to providehardware and software to store, back-up, manage, update, and execute theprovided software. Since SaaS applications cannot access the user'sprivate systems (databases), they often offer integration protocols andapplication programming interfaces (API) such as http (hypertexttransfer protocol), REST (representational state transfer), SOAP (simpleobject access protocol), and JSON (JavaScript Object Notation).

CONCLUSION

The words or terms used herein have their plain, ordinary meaning in thefield of this disclosure, except to the extent explicitly and clearlydefined in this disclosure or unless the specific context otherwiserequires a different meaning. If there is any conflict in the usages ofa word or term in this disclosure and one or more patent(s) or otherdocuments that may be incorporated by reference, the definitions thatare consistent with this specification should be adopted.

The words “comprising,” “containing,” “including,” “having,” and allgrammatical variations thereof are intended to have an open,non-limiting meaning. For example, a composition comprising a componentdoes not exclude it from having additional components, an apparatuscomprising a part does not exclude it from having additional parts, anda method having a step does not exclude it having additional steps. Whensuch terms are used, the compositions, apparatuses, and methods that“consist essentially of” or “consist of” the specified components,parts, and steps are specifically included and disclosed.

The indefinite articles “a” or “an” mean one or more than one of thecomponent, part, or step that the article introduces. The terms “and,”“or,” and “and/or” shall be read in the least restrictive sensepossible. Each numerical value should be read once as modified by theterm “about” (unless already expressly so modified), and then read againas not so modified, unless otherwise indicated in context.

Whenever a numerical range of degree or measurement with a lower limitand an upper limit is disclosed, any number and any range falling withinthe range is also intended to be specifically disclosed. For example,every range of values (in the form “from a to b,” or “from about a toabout b,” or “from about a to b,” “from approximately a to b,” and anysimilar expressions, where “a” and “b” represent numerical values ofdegree or measurement) is to be understood to set forth every number andrange encompassed within the broader range of values.

While the foregoing written description of the disclosure enables one ofordinary skill to make and use the embodiments discussed, those ofordinary skill will understand and appreciate the existence ofvariations, combinations, and equivalents of the specific embodiments,methods, and examples herein. The disclosure should therefore not belimited by the above described embodiments, methods, and examples. Whilethis disclosure has been described with reference to illustrativeembodiments, this description is not intended to be construed in alimiting sense. Various modifications and combinations of theillustrative embodiments as well as other embodiments of the disclosurewill be apparent to persons skilled in the art upon reference to thedescription. It is, therefore, intended that the appended claimsencompass any such modifications or embodiments.

The particular embodiments disclosed above are illustrative only, as thepresent disclosure may be modified and practiced in different butequivalent manners apparent to those skilled in the art having thebenefit of the teachings herein. It is, therefore, evident that theparticular illustrative embodiments disclosed above may be altered ormodified and all such variations are considered within the scope of thepresent disclosure. The various elements or steps according to thedisclosed elements or steps can be combined advantageously or practicedtogether in various combinations or sub-combinations of elements orsequences of steps to increase the efficiency and benefits that can beobtained from the disclosure. It will be appreciated that one or more ofthe above embodiments may be combined with one or more of the otherembodiments, unless explicitly stated otherwise. Furthermore, nolimitations are intended to the details of construction, composition,design, or steps herein shown, other than as described in the claims.

It is claimed:
 1. A method for managing an injection operation in aninjection well having a wellbore extending through a targeted injectionzone in a subterranean reservoir, wherein the injection operation isperformed according to an initial injection schedule having a pluralityof operational parameters, the method comprising: receiving, at acontrol computer having a non-transitory memory, a processor, and asoftware program executable by the control computer, measured injectiondata, in real time via a computerized network, from sensor equipment atthe well; simulating, using the program, at least one injectionoperation in a computer-modelled reservoir using the real time measureddata; determining, based on the simulated injection operation, using theprogram, at least one desired change to the operational parameters ofthe initial injection schedule; communicating the desired change, viathe network, to the well during continuing of the injection operation.2. The method of claim 1, further comprising: automatically performingat the well, the desired change to the operational parameters, inresponse to communicating the desired change via the network.
 3. Themethod of claim 2, further comprising communicating the desired changeto at least one controller operably connected to at least one piece ofsurface equipment at the well.
 4. The method of claim 1, furthercomprising iteratively simulating injection operations having varyingoperational parameters.
 5. The method of claim 1, further comprising,using the program, simulating a modified injection operation comprisinga simulation of the initial injection modified by the real time measuredinjection data.
 6. The method of claim 5, further comprising predicting,using the program, in response to simulating the modified injectionoperation, a loss of containment in the targeted injection zone.
 7. Themethod of claim 6, wherein the loss of containment is predicted by themodified injection operation simulation as resulting from one of: apredicted fracture extending outside of the targeted injection zone; apredicted injection fluid migration outside of the targeted injectionzone; and a predicted stiffness containment breach wherein an upperboundary zone overlying the targeted injection zone, is of higherstiffness than the injection zone.
 8. The method of claim 5, furthercomprising predicting, using the program, and in response to simulatingthe modified injection operation, a modified maximum disposal capacitygreater than a predicted initial disposal capacity corresponding to thesimulated initial injection schedule.
 9. The method of claim 8, whereindetermining at least one desired change to the operational parameters ofthe initial injection schedule further comprises: determining anoperational parameter change predicted to increase the maximum disposalcapacity of the injection operation.
 10. The method of claim 8, whereincommunicating the desired change, via the network, to the well duringcontinuing injection operations further comprises: communicating anoperational parameter change predicted to increase the maximum disposalcapacity of the injection operation.
 11. The method of claim 6, whereindetermining at least one desired change to the operational parameters ofthe initial injection schedule further comprises: determining anoperational parameter change predicted to prevent loss of containment.12. The method of claim 6, wherein communicating the desired change, viathe network, to the well during continuing injection operations furthercomprises: communicating an operational parameter change predicted toprevent loss of containment.
 13. The method of claim 6, furthercomprising: automatically, in response to communicating the desiredchange in operating parameters to the well during continuing of theinjection operation, operating surface equipment at the well using anetworked equipment controller responsive to the communication.
 14. Themethod of claim 13, further comprising preventing a loss of containmentin response to the automatic operation of the surface equipment.
 15. Themethod of claim 13, wherein automatically operating surface equipmentresults in a change of operational parameters correlating to thedetermined, desired change to the operational parameters.
 16. The methodof claim 1, further comprising: continuously communicating real timemeasured data at the well to the control computer; and continuouslysimulating injection operations based on the communicated measured data.17. The method of claim 1, further comprising: automatically operatingsurface equipment at the well to implement the desired change to theoperational parameters only after receiving a user authorization inputto the control computer.
 18. The method of claim 1, wherein simulatingat least one injection operation in a computer-modelled reservoir usingthe real time measured data further comprises, using the program:running injection operation simulations based on known or estimatedformation parameters, zonal rock properties, lithography, zonalstresses, or modelled reservoir, well, and injection simulations. 19.The method of claim 1, wherein the operational parameters injection flowrates, slurry viscosity, slurry density, injection times, or rest times.20. The method of claim 1, further comprising: calculating an OperatingWindow, defined as the difference between a well MASIP and a dynamic,well-head pressure obtained while pumping at a maximum scheduled pumprate.
 21. The method of claim 1, wherein the desired change inoperational parameters is determined by running iterative simulationswith varying operational parameters.
 22. The method of claim 1, whereinthe desired operational change is verified by the program based oncommunicated, real time measured data.
 23. The method of claim 1,further comprising: using the program and input, planned BatchInjections, maximizing overall Disposal Capacity of the well across theplanned Batch Injections.
 24. The method of claim 23, furthercomprising: using the program, determining a desired operationalparameter change selected to maximize overall disposal capacity of thewell in response to a simulation prediction, based on the real timemeasured data, indicates damage to the reservoir negatively effectingoverall disposal volume.